Spiking Neural Belief Propagation Decoder for LDPC Codes with Small Variable Node Degrees

TL;DR

This paper proposes the ML-ELENA-SNN decoder, an extension of previous SNN-based LDPC decoders, which uses multiple SNNs in parallel to improve decoding accuracy for codes with small variable node degrees, matching traditional methods.

Contribution

The paper introduces the multi-level ELENA-SNN decoder that enhances approximation accuracy by employing multiple SNNs in parallel for LDPC codes with small variable node degrees.

Findings

Performs similarly to normalized min-sum decoder on tested LDPC code

Uses multiple SNNs to improve message approximation accuracy

Effective for LDPC codes with small variable node degrees

Abstract

Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-likelihood-each-notable-amplitude spiking-neural-network (ELENA-SNN) decoder, a novel decoding algorithm for low-density parity-check (LDPC) codes. The decoder integrates SNNs into belief propagation (BP) decoding by approximating the check node (CN) update equation using SNNs. However, when decoding LDPC codes with a small variable node(VN) degree, the approximation gets too rough, and the ELENA-SNN decoder does not yield good results. This paper introduces the multi-level ELENA-SNN (ML-ELENA-SNN) decoder, which is an extension of the ELENA-SNN decoder. Instead of a single SNN approximating the CN update, multiple SNNs are applied in parallel, resulting in a higher resolution and higher dynamic range of the exchanged messages. We show that the ML-ELENA-SNN decoder performs similarly to the ubiquitous normalized min-sum decoder for the (38400, 30720) regular LDPC code with a VN degree of dv = 3 and a CN degree of dc = 15.